Detection of Material Surface Cracks by Infrared Non-destructive Testing

Yuan, Lihua; Xiao, Zhu; Hong, Kang

doi:10.1109/phm-jinan48558.2020.00114

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…This active IRT detection is suitable because there is usually no obvious natural temperature difference between targets and their back-ground when testing samples without active excitation [3,4]. IRT detection exhibits the advantages of high detection efficiency and rich and complete detection information [5,6]. However, the heating excitation method [7], the type of infrared camera, and the processing method of infrared signals and infrared thermal images can generally have an effect on the accuracy of infrared non-destructive testing [8].…”

Section: Introductionmentioning

confidence: 99%

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Gou

Qingying

Yao

et al. 2023

Meas. Sci. Technol.

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Active infrared thermography has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis algorithm with sliding-window mechanism and the 1DResnet50-CBAM (One Dimensional - Residual Neural Network - Convolutional Block Attention Module) model was proposed for the reconstructed image sequence of active infrared thermography to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using principal component analysis algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional (3D) defect data were transformed into one-dimensional (1D) temporal infrared thermal signal. Moveover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion principal component analysis algorithm with sliding-window mechanism and 1DResnet50-CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and quantitative analysis using infrared thermal detection technology.

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Section: Introductionmentioning

confidence: 99%

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Gou

Qingying

Yao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In 2016, Zou and Luo [25] carried out theoretical analysis and experimental verification by using infrared thermal image to analyze the crack damage of tunnel surrounding rock under blasting action. In 2017, Yuan et al [26] realized the detection of surface crack of impact damage of carbon fiber laminate using infrared thermal image. It can be seen that infrared thermal radiation image has the characteristics of realizing on-line non-contact detection and feature image is more intuitive.It can be applied to fault detection of various field equipment as well as defect and corrosion detection of various materials [27,28].…”

Section: Introductionmentioning

confidence: 99%

Infrared spectrum analysis method for detection and early warning of longitudinal tear of mine conveyor belt

et al. 2020

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Active Online Monitoring for Main Shaft Slight Crack in Rotating Electric Drive System Based on Chirp Torque Injection

Wang,

et al. 2023

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

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Detection of Material Surface Cracks by Infrared Non-destructive Testing

Cited by 3 publications

References 4 publications

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Infrared spectrum analysis method for detection and early warning of longitudinal tear of mine conveyor belt

Active Online Monitoring for Main Shaft Slight Crack in Rotating Electric Drive System Based on Chirp Torque Injection

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